@article{jcsa2015311,
author={{Yokota, Satoru and Li, Jiang and Ogishima, Yuichi and Kubo, Hiromasa and Tamukoh, Hakaru and Sekine, Masatoshi},
title={Self-Learning of Feature Regions for Image Recognition},
journal={Journal of Computer Sciences and Applications},
volume={3},
number={1},
pages={1--10},
year={2015},
url={http://pubs.sciepub.com/jcsa/3/1/1},
issn={2328-725X},
abstract={Mobile systems are used in various environments. Thus, it is practical for image recognition systems to autonomously learn template images that are adaptive to objects in their various environments. However, learning the features of such objects requires large-scale computation and complex control. Hence, we propose an image recognition system that selects and learns regions that have a given object's features. This system is designed as a hardware/software (hw/sw) complex system with the multi-dimensional field programmable gate array (FPGA) ˇ°Vocalise.ˇ± This study discusses the possibility of dynamically building image databases and of real-time learning using the proposed image recognition system. Results indicate that the learning speed of the proposed method is estimated to be 1.4 ˇÁ 10<SUP>3</SUP> faster than that obtained with a conventional software method. This suggests the possibility of real-time learning.},
doi={10.12691/jcsa-3-1-1}
publisher={Science and Education Publishing}
}